Operational modal analysis with compressed measurements based on prior information

Abstract

Compressive Sensing (CS) provides a solution for modal analysis with fewer/compressed measurements. However, operational modal analysis by using compressed measurements directly has not been studied. In this work, a new approach is proposed, which deploys the identified modal parameters from the most recent step as prior information to automatically set the frequency and damping ratio search ranges and uses CS to identify modal parameters from compressed measurements. Numerical studies on a spring-mass system and experimental studies on a wind turbine model are performed to demonstrate the effectiveness of the proposed approach. The results are accurate on damped structures including those with heavy noise and complex responses, under free or random vibration conditions. Its performance using compressed measurements is comparable to those of state-of-the-art methods using original uncompressed measurements. It may find practical applications as an efficient and accurate operational modal analysis tool for online structural health monitoring.